Events as Subsets of a Sample Space: Unions, Intersections...

Section 1

Why We Need a Language for Random Events

Before mathematicians had a shared vocabulary for probability, arguments about dice, card games, and insurance payouts were resolved by intuition—and intuition often failed. The development of set theory as a foundation for probability took centuries. Understanding this backstory shows you why we define events as subsets rather than vague ideas like "the chance of rain."

1654

Blaise Pascal and Pierre de Fermat exchanged letters about splitting stakes in an interrupted dice game—the famous "Problem of Points." Their correspondence launched formal probability reasoning, though they had no set-theoretic language yet.

1812

Pierre-Simon Laplace published Théorie analytique des probabilités, defining probability as the ratio of "favorable cases" to "all equally possible cases." This classical definition works only for finite, equally likely outcomes, but it planted the seed: you must clearly list what's possible before calculating any probability.

1874–1897

Georg Cantor developed modern set theory—the idea that collections of objects (sets) obey precise rules for union, intersection, and complementation. This toolkit became the backbone of probability.

1933

Andrey Kolmogorov published Foundations of the Theory of Probability, axiomatically grounding probability on set theory. He defined a sample space S, declared events to be subsets of S, and assigned probabilities to those subsets using three simple axioms. This framework is still the world standard.

Kolmogorov's insight was elegant: once you model all possible outcomes as a universal set, every question you can ask about "what might happen" corresponds to a well-defined subset. Unions, intersections, and complements let you combine simple events into complex ones without ambiguity. That is exactly what this lesson teaches you to do.

Section 2

Core Definitions & Principles

At the heart of probability are three closely related ideas: the sample space, outcomes, and events. Once you grasp how these three fit together, every other probability concept—from conditional probability to independence—builds on top of them.

Sample Space (S)

The sample space is the set of all possible outcomes of a random experiment. If you roll a standard die, S = {1, 2, 3, 4, 5, 6}. Every probability question you can ask lives inside S.

Outcome

An outcome is a single, indivisible result of an experiment—one element of S. When you flip a coin, "Heads" is an outcome. Outcomes cannot be broken down further within that experiment.

Event

An event is any subset of S—including the empty set ∅ and S itself. "Rolling an even number" is the event E = {2, 4, 6}. An event occurs if the actual outcome belongs to that subset.

Describing Events

You can describe events by characteristics of outcomes (e.g., "even," "red," "greater than 3") or by combining simpler events with union (∪), intersection (∩), and complement (′).

✦ Key Takeaway

Think of the sample space as the entire menu at a restaurant. Each individual dish is an outcome. An event is like saying "I want something vegetarian"—you've circled a group of dishes that share a characteristic. Union is "vegetarian or gluten-free," intersection is "vegetarian and gluten-free," and complement is "everything that is not vegetarian." Set operations let you describe any combination of preferences precisely.

Section 3

Venn Diagram: Visualizing Events Inside S

The most powerful visual for understanding events as subsets is the Venn diagram. In the diagram below, the large rectangle represents the sample space S for drawing one card from a standard 52-card deck. Two events are shown: A = "the card is a heart" and B = "the card is a face card (J, Q, K)." Notice how the overlapping region represents the intersection A ∩ B—the face cards that are also hearts.

Figure 1 — Events A (hearts) and B (face cards) as overlapping subsets of the 52-card sample space S.

Every region in the Venn diagram corresponds to a subset you can name using set operations. The left crescent (in A but not in B) is A ∩ B′. The right crescent is A′ ∩ B. The combined shaded area is the union A ∪ B, and everything outside both circles is the complement (A ∪ B)′. Being able to point to a region in the diagram and write its set expression—and vice versa—is the skill this lesson develops.

Section 4

Set Operations & Their Probability Rules

Three fundamental set operations let you build any event from simpler pieces. Each has a direct translation into probability language and a formula for computing probabilities.

Union ("Or")

A ∪ B = { x ∈ S : x ∈ A or x ∈ B }

The event "A or B (or both) occurs." Includes every outcome that is in at least one of the two events.

Intersection ("And")

A ∩ B = { x ∈ S : x ∈ A and x ∈ B }

The event "both A and B occur simultaneously." Only outcomes common to both events.

Complement ("Not")

A′ = { x ∈ S : x ∉ A }

The event "A does not occur." Every outcome in S that is not in A. Also written Aᶜ or Ā.

The Addition Rule

When you take the union of two events, you must be careful not to double-count outcomes that belong to both. This gives the Addition Rule:

Addition Rule

P(A ∪ B) = P(A) + P(B) − P(A ∩ B)

Subtract the intersection because those outcomes were counted once in P(A) and once in P(B).

If A and B are mutually exclusive (they share no outcomes, so A ∩ B = ∅), then P(A ∩ B) = 0 and the formula simplifies to P(A ∪ B) = P(A) + P(B). The complement rule follows directly from the fact that A and A′ partition S: P(A′) = 1 − P(A).

✦ Key Takeaway

Union means "or," intersection means "and," and complement means "not." These three words correspond precisely to three set operations. If you can rephrase a probability question using "or," "and," and "not," you can immediately translate it into set notation—and then into a formula.

Section 5

Describing Events by Characteristics & Categories

One of the most practical skills in probability is describing events using characteristics of the outcomes rather than listing every element. When you say "the card drawn is red," you are partitioning the sample space by the characteristic color and selecting one category. When you say "the die shows a number greater than 4," you're using the characteristic value with an inequality criterion. Combining characteristics with set operations lets you express highly specific events concisely.

Consider rolling two six-sided dice. The sample space S has 36 outcomes—each an ordered pair (d₁, d₂) where d₁ and d₂ range from 1 to 6. Rather than listing all 36 pairs, you can describe events by characteristics of the sum, the individual dice, or their relationship.

Figure 2 — The 36-outcome sample space for two dice. Cyan = A (sum = 7), Violet = B (d₁ is even), Green = A ∩ B (both).

In Figure 2, event A = "the sum equals 7" is defined by a characteristic of the pair—the sum. Event B = "the first die is even" is defined by a category of a single die. The green cells show the intersection A ∩ B, outcomes where the sum is 7 and d₁ is even: {(2,5), (4,3), (6,1)}. This grid-based approach is especially handy for two-dice problems because every cell has equal probability 1/36.

Set Expression	English Description	Outcomes	Count
`A`	Sum equals 7	(1,6) (2,5) (3,4) (4,3) (5,2) (6,1)	6
`B`	First die is even	All (2,_), (4,_), (6,_)	18
`A ∩ B`	Sum = 7 and d₁ even	(2,5) (4,3) (6,1)	3
`A ∪ B`	Sum = 7 or d₁ even (or both)	All of B plus (1,6) (3,4) (5,2)	21
`A′`	Sum ≠ 7	All 30 remaining pairs	30
`(A ∪ B)′`	Sum ≠ 7 and d₁ odd	All odd-d₁ pairs with sum ≠ 7	15

Section 6

Worked Example: Survey Data

A class of 40 students was surveyed about two extracurricular activities. Define events: M = "plays a musical instrument" (18 students), S = "plays a sport" (22 students), and 8 students do both. Find P(M ∪ S), P(M ∩ S′), and P(M′ ∩ S′).

Worked Example: Survey Data

Step 1 — Identify the Sample Space and Given InformationThe sample space consists of the 40 students, so |S| = 40. We know |M| = 18, |S| = 22, and |M ∩ S| = 8. Each student is equally likely to be selected at random.

Step 2 — Find P(M ∪ S) Using the Addition RuleApply P(M ∪ S) = P(M) + P(S) − P(M ∩ S).

P(M ∪ S) = 18/40 + 22/40 − 8/40 = 32/40 = 0.80. So 80% of students participate in at least one of the two activities.

Step 3 — Find P(M ∩ S′): Plays Music but NOT a SportThe event M ∩ S′ captures students who play an instrument but do not play a sport. Since |M| = 18 and 8 of those also play a sport, the "music only" count is 18 − 8 = 10.

P(M ∩ S′) = 10/40 = 0.25

Step 4 — Find P(M′ ∩ S′): Neither ActivityThe complement of the union gives us students in neither event: M′ ∩ S′ = (M ∪ S)′. Since |M ∪ S| = 32, the number outside both is 40 − 32 = 8.

P(M′ ∩ S′) = 8/40 = 0.20

Step 5 — Verify: Check That All Regions Sum to 1Music only: 10. Both: 8. Sports only: 22 − 8 = 14. Neither: 8. Total: 10 + 8 + 14 + 8 = 40. ✓

The four mutually exclusive regions of the Venn diagram account for every student in the sample space, confirming our calculations are consistent.

Section 7

Strengths, Limitations & Common Pitfalls

Strength	Limitation / Pitfall
Set notation is unambiguous—every event is precisely defined, eliminating confusion about what "or" means.	Students often confuse everyday "or" (exclusive) with mathematical "or" (inclusive). A ∪ B includes outcomes in both A and B.
Venn diagrams provide a visual check that regions partition S without overlap or gaps.	Venn diagrams become unwieldy for more than three events. With four or more events, tables or systematic listing may be clearer.
The Addition Rule handles any two events, whether overlapping or mutually exclusive.	Forgetting to subtract P(A ∩ B) is the most common arithmetic error—double-counting inflates the probability.
Describing events by characteristics scales naturally to large sample spaces (e.g., "red cards" vs. listing 26 cards).	When outcomes are not equally likely, you must use probabilities rather than simple counting. This approach assumes you know the probability model.
De Morgan's Laws provide a bridge: (A ∪ B)′ = A′ ∩ B′ and (A ∩ B)′ = A′ ∪ B′.	Misapplying De Morgan's Laws—for example, writing (A ∪ B)′ = A′ ∪ B′—is a frequent mistake. Always negate and flip the operation.

✦ Key Takeaway

Think of De Morgan's Laws like a light switch that flips two things at once: when you "push through" the complement, every union becomes an intersection and every intersection becomes a union. If you're looking for "not (A or B)," you need "not A and not B." It's like saying "I want neither pizza nor tacos" means "no pizza AND no tacos," not "no pizza or no tacos."

Section 8

Connection to Conditional Probability & Independence

Everything you've learned about events as subsets feeds directly into two major topics you'll encounter next: conditional probability and independence. Conditional probability asks: given that event B has occurred, what is the probability of event A? In set language, you're restricting your sample space from S to just B, and then looking at the fraction of B that also falls in A.

Conditional Probability

P(A | B) = P(A ∩ B) / P(B), provided P(B) > 0

The intersection A ∩ B is the numerator—without knowing how to define intersections, this formula would be meaningless.

Two events A and B are independent if knowing that B occurred does not change the probability of A. Algebraically, this means P(A ∩ B) = P(A) × P(B). Independence is a property of how two subsets relate within S—it cannot even be stated without the subset framework you've just learned.

Concept	This Lesson	Next Topic
Event definition	Subset of S described by characteristics or set operations	Same, but now we condition—restrict—the "universe" to a smaller subset
Intersection	A ∩ B: outcomes in both A and B	Numerator of conditional probability; test for independence
Complement	A′ = S \ A	Used in Bayes' Theorem via P(A′) = 1 − P(A)
Union	A ∪ B with Addition Rule	Extended to three or more events; Inclusion–Exclusion Principle

Mastering the idea that events are subsets gives you the precise language needed for every probability argument going forward. Without it, conditional probability and independence would be hand-waving; with it, they become clean algebraic statements about sets.

Section 9

Practice Problems

PROBLEM 1 — CONCEPTUAL

Explain in your own words why the event "rolling a 7 on a single standard six-sided die" corresponds to the empty set ∅ as a subset of S = {1, 2, 3, 4, 5, 6}. What is P(∅)?

PROBLEM 2 — BASIC IDENTIFICATION

A bag contains 5 red marbles, 3 blue marbles, and 2 green marbles. You draw one marble at random. Define events R = "red," B = "blue," and G = "green." Write R ∪ G as a subset of S using roster notation, and find P(R ∪ G).

PROBLEM 3 — INTERMEDIATE

Two dice are rolled. Let A = "the sum is at least 9" and B = "both dice show odd numbers." Find A ∩ B and compute P(A ∩ B). Then find P(A ∪ B) using the Addition Rule.

PROBLEM 4 — APPLIED / MULTI-STEP

At a school of 200 students, 120 take Chemistry, 90 take Physics, and 50 take both. A student is selected at random. Find the probability that the student takes exactly one of the two courses. Express your answer as a set operation first, then compute.

PROBLEM 5 — SYNTHESIS

For any two events A and B in a sample space S, prove algebraically that P(A ∩ B′) = P(A) − P(A ∩ B). Then explain why this result makes intuitive sense using a Venn diagram argument.

Summary

Lesson Summary

In this lesson you learned that every probability question begins with a sample space S—the complete set of possible outcomes—and that any event is simply a subset of S. Events can be described by characteristics or categories of the outcomes (such as "even," "red," or "sum greater than 9") rather than by listing each outcome individually. Three fundamental set operations let you build complex events from simple ones: the union (A ∪ B) captures "A or B or both," the intersection (A ∩ B) captures "A and B," and the complement (A′) captures "not A."

The Addition Rule P(A ∪ B) = P(A) + P(B) − P(A ∩ B) prevents double-counting, while De Morgan's Laws let you rewrite complements of unions as intersections of complements (and vice versa). Venn diagrams serve as a visual check that your subsets partition S without overlap or gaps. These ideas form the essential foundation for conditional probability and independence—the topics you'll study next. Master the language of sets, and every probability formula becomes a statement about well-defined collections of outcomes.